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Estimating Number of Clusters Based on a General Similarity Matrix with Application to Microarray Data

Author

Listed:
  • Fallah Shafagh

    (University of Toronto)

  • Tritchler David

    (University Health Network, Toronto; University of Toronto; and SUNY at Buffalo)

  • Beyene Joseph

    (Hospital for Sick Children Research Institute and University of Toronto)

Abstract

Many clustering methods require that the number of clusters believed present in a given data set be specified a priori, and a number of methods for estimating the number of clusters have been developed. However, the selection of the number of clusters is well recognized as a difficult and open problem and there is a need for methods which can shed light on specific aspects of the data. This paper adopts a model for clustering based on a specific structure for a similarity matrix. Publicly available gene expression data sets are analyzed to illustrate the method and the performance of our method is assessed by simulation.

Suggested Citation

  • Fallah Shafagh & Tritchler David & Beyene Joseph, 2008. "Estimating Number of Clusters Based on a General Similarity Matrix with Application to Microarray Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-25, August.
    • Handle: RePEc:bpj:sagmbi:v:7:y:2008:i:1:n:24
    DOI: 10.2202/1544-6115.1261
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    References listed on IDEAS

    as
    1. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    2. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
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    Cited by:

    1. Tao Li & Yi Zhang & Dingding Wang & Jian Xu, 2019. "MCC: a Multiple Consensus Clustering Framework," Journal of Classification, Springer;The Classification Society, vol. 36(3), pages 414-434, October.

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